All approaches use the ordinary non-parametric bootstrap to estimate the variance of the \(A\)  vs. \(C\)  marginal treatment effect. 2,000 resamples of each simulated dataset are drawn with replacement [1]}, [2]}. Due to patient-level data limitations for the competitor study, only the IPD of the index trial are resampled in the implementation of the bootstrap. The average marginal mean difference for \(A\)  vs. \(C\)  in \(S=2\)  is computed as the average across the bootstrap resamples. Its standard error is the standard deviation across these resamples. For the “one-stage” MAIC approaches, each bootstrap iteration re-estimates the trial assignment model. For the “two-stage” MAIC approaches, both the trial assignment and the treatment assignment model are re-estimated in each iteration.
